Shulou(Shulou.com)05/31 Report--

This article mainly introduces "how to draw bars and histograms in Python+matplotlib". In daily operation, I believe many people have doubts about how to draw bars and histograms in Python+matplotlib. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts about "how to draw bars and histograms by Python+matplotlib". Next, please follow the editor to study!

1. Bar () function

The main parameters of the bar () function are as follows:

Bar (x, height, width=0.8, bottom=None, *, align='center', data=None, * kwargs)

Parameter 1RV x: scalar type, coordinates on the x axis. Floating point number or class array structure. Note that x can be an array of strings

The coordinates on the parameter 2:height:y axis. Floating point number or class array structure

Parameter 3:width: specifies the width of the column chart. Floating point number or class array structure. The default value is 0.8

Parameter 4:bottom: scalar or scalar class array type, starting height of y coordinate

Parameter 5:align: alignment of the bar chart on the x axis. Optional {'center',' edge'} center: Center symmetry edge: edge symmetry

Parameter 6:**kwargs: the received keyword parameter is passed to the associated Rectangle. Return value: BarContainer instance whose patches property is a list of columns

Bar chart (bar chart) A simple example of randomly setting 12 months and giving some data, the code is as follows:

Plt.bar ([1 Test one ", color='red') # x the number of columns on the position [1, 2, 3, 4, 5, 5, 4, 5, 5, 4, 5, 5, 7, 9, 9, 10, and 12], which is expressed as the relative y-axis, the position of the bar chart in the X-axis, and the height of the corresponding y-axis. Plt.legend () # the legend name in the run result shows plt.xlabel ('bar number') plt.ylabel (' bar height') plt.title ('TEST') plt.show ()

The color='red' in the first column indicates that the bar chart is all shown in red. For display difference, make the following changes:

Import matplotlib.pyplot as plt plt.bar ([1 label= 3 5 label= 7 color 9], [5 color 7 7 Test two, color='g') # color can also be hexadecimal. As shown in # 202204plt.legend () # the legend name shows plt.xlabel ('bar number') plt.ylabel (' bar height') plt.title ('TEST') plt.show ()

The specific display results are as follows:

Second, hist () function

Hist (bar, align= "mid", orientation= "vertical", rwidth=None,log=False,color=None,label=None,stacked=False,normed=None, hold=None,data=None,**kwargs)

The basic parameters of the hist () function are as follows:

X: represents the input value, which can be a single array or an array sequence of the same length.

Bins: represents the number of columns drawn. Given an integer, "bins+1" columns are returned. The default is 10.

The upper and lower range of the range:bins (maximum and minimum values).

Color: represents the color of the bar. The default is None.

Facecolor # histogram color

Edgecolor # histogram border color

Alpha # Transparency

Histtype # histogram type, 'bar',' barstacked', 'step',' stepfilled'

Orientation # horizontal or vertical

The distance between rwidth # posts and columns. Default is 0.

Here is an example to illustrate the function of the hist () function:

Import matplotlib.pyplot as plt population_ages = [18, bins, 23, 26, 14, 14, 14, 99, 99, 63105121116] # sets a group of age bins = [01010121116] # sets a group of ages bins = [010, bins, 30, 40, 50, 80, 90100130] # Age segments plt.hist (population_ages, bins, histtype='bar', rwidth=0.8,color='#199209') plt.xlabel ('The Age Group') plt.ylabel (' The number') plt.title ('The Age Range') plt.legend () plt.show ()

Note: the lack between 60, 80 and 100130 in bins [] is intentional, in order to show the difference.

The running results are as follows:

Obviously, the hist () function automatically counts the data in parameter x based on the distinction in parameter bins.

Cause trouble, since the parameter x (such as population_ages in the example) may be a series, can you use a random function to automatically generate an array and then count it? Yes, of course.

III. Data statistics

Dice is often used for entertainment (used for consequences caused by other ways has nothing to do with me), it has 6 faces, respectively, the number of points is 1, 2, 3, 4, 4, 5, and 6, it can be randomly generated by random functions (choice function in the previous article), such as choice ([1, 2, 3, 4, 5, and 6]), generate N times (for example, 200000 times) and save each result to the list, and finally count the total number of points or the proportion of points.

Analysis:

1) first build a class whose function is to run once, then randomly select 6 faces (points)

2) instantiate the dice and give a parameter (the number of runs) and graphically display it.

Class Sezi (): def _ init__ (self,sides): # define a number of faces for yourself so that you can modify the parameters later for other operations self.sides = sides # dice can be 6 faces, or 8 faces, 10 faces, 12 sides, requiring a given def roll (self): return choice ([1 recorder, 2 init__, 3, 4, 5) # each cast, randomly select a point testsezi = Sezi (6) # instantiation 6 faces results = [] # define an empty sequence, which is used to save the points of each throw for roll_num in range (100): # loop, throw result = testsezi.roll () # save the result of each throw to the variable result results.append (result) # save it to the sequence resultsprint (results) # print it out directly

Running result:

At the same time, for convenience, another random function randint (x, y) is introduced. The function of this function is to produce a number between xmury, such as randint (1jie 10), which produces a number between 1 and 10.

Choice can be modified to randint, so that after instantiation, you need to enter a random number of faces, and the corresponding number will be randomly generated.

The above is just printed in the interaction bar, and the classes and instances are still in one file, divided into different files, and the data statistics are displayed in the form of a graph.

1. Modify the dice subclass again

The file name is sezi.py, and the code is as follows:

From random import * class Sezi (): def _ init__ (self,sides): # define a number of faces for yourself. The number of faces corresponds to self.sides = sides # the dice can be 6 faces, or 8 faces, 10 faces, 12 faces. You need to give self.side=0 self.bins= [] while self.side < self.sides: # to get the number of faces, and get a bins of the number of faces, which can be called directly. Self.side + = 1 self.bins.append (self.side) def roll (self): return randint (1 recordself.sides) # each throw, randomly select a point

two。 Create a new file named sezigame.py with the following code

Import matplotlib.pyplot as pltfrom sezi import * testsezi = Sezi (8) # instantiate, 8 points results = [] # define an empty sequence to hold the number of points thrown each time for roll_num in range (50000): # loop Throw 50000 times result = testsezi.roll () # save the result of each throw to the variable result (result) # into the sequence resultsplt.hist (results, testsezi.bins, histtype='bar', rwidth=0.8,color='#199209') # call testsezi.bins directly

Running result:

What if there are two identical dice?

If you roll two dice at the same time, the minimum is 2 and the maximum is 12, the result distribution is naturally different.

Modify the file named sezigame.py, and the changed code is as follows:

Import matplotlib.pyplot as pltfrom sezi import * sezi_1 = Sezi (6) # instantiate, 6 faces sezi_2 = Sezi (6) results = [] # define an empty sequence to hold the number of points thrown each time for roll_num in range (50000): # loop Throw 50000 times result = sezi_1.roll () + sezi_2.roll () # Save the result of two throws into the variable result results.append (result) # into the sequence resultsmax_result = sezi_1.sides+sezi_2.sides # 2 maximum 12, minimum 2 side = 0 new_bins = [] while side

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